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motleycrew

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Multi-Agent Platforms & Frameworks
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Basic Information

motleycrew is a Python framework for building and orchestrating multi-agent AI systems. It is designed to let developers mix and match agents and tools from popular ecosystems to create complex workflows while managing execution details. The repository provides building blocks such as agents, tools, tasks and a knowledge graph backend that stores tasks and execution data. Components implement Langchain's Runnable API for compatibility with LCEL and integrations include Langchain, LlamaIndex, CrewAI, and Autogen. The project also integrates caching and observability tools to simplify debugging and testing. The README includes installation instructions, a quickstart example showing a writer and illustrator task chain, and pointers to documentation and example flows like a research agent and a blog with images.

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App URL
https://github.com/MotleyAI/motleycrew
Github Repository
https://github.com/MotleyAI/motleycrew/blob/main/README.md

App Details

Features
Integration with multiple agent and tool ecosystems including Langchain, LlamaIndex, CrewAI and Autogen. A flexible design that allows agents to be provided with tools or other agents and makes components compatible with Langchain Runnable API. Advanced flow composition using a knowledge graph that stores tasks and execution data and enables custom task behaviors and flow control. Simple task primitives such as SimpleTask and a chaining operator to sequence tasks. Built-in support for HTTP and LLM call caching via motleycache and observability integration with Lunary. Documentation, examples, a quickstart, and installable package available via pip.
Use Cases
motleycrew helps developers focus on high level design by handling orchestration, data storage and integration details for multi-agent AI systems. It reduces implementation overhead by providing reusable task and agent abstractions, an internal knowledge graph for state and metadata, and compatibility with existing toolchains through the Runnable API. Caching of HTTP and LLM calls speeds up debugging and testing, while observability integration enables monitoring and visualization of agent workflows. Example workflows illustrate common patterns such as chained writer and illustrator tasks and research agents, enabling faster prototyping. The repository also provides documentation, examples and community support for extending and customizing agent behaviors.

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